Identification of product life cycle models by autoregression–moving average models and Groebner’s bases
AbstractThe authors offer the analytical models of product life cycle and the approach towards their classification based on the models of autoregression–moving average and using the Groebner bases for solving the normal systems of non-linear polynomial equations, received after using the least-squares method. The characteristics of modeling and forecasting fidelity have been also elaborated, concerning the sales data for cars, data for oil production, as well as interest of Google users towards cell phone models and guide-books edition.
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Bibliographic InfoArticle provided by Publishing House "SINERGIA PRESS" in its journal Applied Econometrics.
Volume (Year): 25 (2012)
Issue (Month): 1 ()
Contact details of provider:
Web page: http://appliedeconometrics.cemi.rssi.ru/
product life cycle models; ARMA; OLS method; Groebner bases; car; cell phone; guidebook;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- D91 - Microeconomics - - Intertemporal Choice and Growth - - - Intertemporal Consumer Choice; Life Cycle Models and Saving
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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"Comparing Predictive Accuracy,"
NBER Technical Working Papers
0169, National Bureau of Economic Research, Inc.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
- Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
- Beveridge, Stephen & Nelson, Charles R., 1981. "A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the `business cycle'," Journal of Monetary Economics, Elsevier, vol. 7(2), pages 151-174.
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